Vehicle detection is a critical task in modern Intelligent Traffic Systems. To facilitate traffic monitoring and control, ITS systems generally require input of traffic information, such as traffic density, queue length, vehicle counting, vehicle position, vehicle velocity and vehicle type from a Traffic Scene Analysis (TSA) component. The more a TSA component can offer, the better the traffic state estimation can be achieved.
The increasing demand for the capability of producing rich information is the driving factor for combining Computer-Vision based systems with the traditional inductive-loop-based systems in recent years.
In the past two decades, many methods for vehicle detection in the context of traffic surveillance have been proposed in the computer vision community. Based on their technical approaches, they can be roughly divided into six categories:                background subtraction;        supervised-learning-based detector;        feature point detector;        foreground segmentation;        color segmentation; and        vehicle model matching.        